12 research outputs found
Multibeam Electron Diffraction
One of the primary uses for transmission electron microscopy (TEM) is to
measure diffraction pattern images in order to determine a crystal structure
and orientation. In nanobeam electron diffraction (NBED) we scan a moderately
converged electron probe over the sample to acquire thousands or even millions
of sequential diffraction images, a technique that is especially appropriate
for polycrystalline samples. However, due to the large Ewald sphere of TEM,
excitation of Bragg peaks can be extremely sensitive to sample tilt, varying
strongly for even a few degrees of sample tilt for crystalline samples. In this
paper, we present multibeam electron diffraction (MBED), where multiple probe
forming apertures are used to create mutiple STEM probes, all of which interact
with the sample simultaneously. We detail designs for MBED experiments, and a
method for using a focused ion beam (FIB) to produce MBED apertures. We show
the efficacy of the MBED technique for crystalline orientation mapping using
both simulations and proof-of-principle experiments. We also show how the
angular information in MBED can be used to perform 3D tomographic
reconstruction of samples without needing to tilt or scan the sample multiple
times. Finally, we also discuss future opportunities for the MBED method.Comment: 14 pages, 6 figure
py4DSTEM: a software package for multimodal analysis of four-dimensional scanning transmission electron microscopy datasets
Scanning transmission electron microscopy (STEM) allows for imaging,
diffraction, and spectroscopy of materials on length scales ranging from
microns to atoms. By using a high-speed, direct electron detector, it is now
possible to record a full 2D image of the diffracted electron beam at each
probe position, typically a 2D grid of probe positions. These 4D-STEM datasets
are rich in information, including signatures of the local structure,
orientation, deformation, electromagnetic fields and other sample-dependent
properties. However, extracting this information requires complex analysis
pipelines, from data wrangling to calibration to analysis to visualization, all
while maintaining robustness against imaging distortions and artifacts. In this
paper, we present py4DSTEM, an analysis toolkit for measuring material
properties from 4D-STEM datasets, written in the Python language and released
with an open source license. We describe the algorithmic steps for dataset
calibration and various 4D-STEM property measurements in detail, and present
results from several experimental datasets. We have also implemented a simple
and universal file format appropriate for electron microscopy data in py4DSTEM,
which uses the open source HDF5 standard. We hope this tool will benefit the
research community, helps to move the developing standards for data and
computational methods in electron microscopy, and invite the community to
contribute to this ongoing, fully open-source project
A Fast Algorithm for Scanning Transmission Electron Microscopy (STEM) Imaging and 4D-STEM Diffraction Simulations
Scanning transmission electron microscopy (STEM) is an extremely versatile
method for studying materials on the atomic scale. Many STEM experiments are
supported or validated with electron scattering simulations. However, using the
conventional multislice algorithm to perform these simulations can require
extremely large calculation times, particularly for experiments with millions
of probe positions as each probe position must be simulated independently.
Recently, the PRISM algorithm was developed to reduce calculation times for
large STEM simulations. Here, we introduce a new method for STEM simulation:
partitioning of the STEM probe into "beamlets," given by a natural neighbor
interpolation of the parent beams. This idea is compatible with PRISM
simulations and can lead to even larger improvements in simulation time, as
well requiring significantly less computer RAM. We have performed various
simulations to demonstrate the advantages and disadvantages of partitioned
PRISM STEM simulations. We find that this new algorithm is particularly useful
for 4D-STEM simulations of large fields of view. We also provide a reference
implementation of the multislice, PRISM and partitioned PRISM algorithms
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A Fast Algorithm for Scanning Transmission Electron Microscopy Imaging and 4D-STEM Diffraction Simulations.
Scanning transmission electron microscopy (STEM) is an extremely versatile method for studying materials on the atomic scale. Many STEM experiments are supported or validated with electron scattering simulations. However, using the conventional multislice algorithm to perform these simulations can require extremely large calculation times, particularly for experiments with millions of probe positions as each probe position must be simulated independently. Recently, the plane-wave reciprocal-space interpolated scattering matrix (PRISM) algorithm was developed to reduce calculation times for large STEM simulations. Here, we introduce a new method for STEM simulation: partitioning of the STEM probe into “beamlets,” given by a natural neighbor interpolation of the parent beams. This idea is compatible with PRISM simulations and can lead to even larger improvements in simulation time, as well requiring significantly less computer random access memory (RAM). We have performed various simulations to demonstrate the advantages and disadvantages of partitioned PRISM STEM simulations. We find that this new algorithm is particularly useful for 4D-STEM simulations of large fields of view. We also provide a reference implementation of the multislice, PRISM, and partitioned PRISM algorithms
Recommended from our members
Multibeam Electron Diffraction.
One of the primary uses for transmission electron microscopy (TEM) is to measure diffraction pattern images in order to determine a crystal structure and orientation. In nanobeam electron diffraction (NBED), we scan a moderately converged electron probe over the sample to acquire thousands or even millions of sequential diffraction images, a technique that is especially appropriate for polycrystalline samples. However, due to the large Ewald sphere of TEM, excitation of Bragg peaks can be extremely sensitive to sample tilt, varying strongly for even a few degrees of sample tilt for crystalline samples. In this paper, we present multibeam electron diffraction (MBED), where multiple probe-forming apertures are used to create multiple scanning transmission electron microscopy (STEM) probes, all of which interact with the sample simultaneously. We detail designs for MBED experiments, and a method for using a focused ion beam to produce MBED apertures. We show the efficacy of the MBED technique for crystalline orientation mapping using both simulations and proof-of-principle experiments. We also show how the angular information in MBED can be used to perform 3D tomographic reconstruction of samples without needing to tilt or scan the sample multiple times. Finally, we also discuss future opportunities for the MBED method
Recommended from our members
Prismatic 2.0 - Simulation software for scanning and high resolution transmission electron microscopy (STEM and HRTEM).
Scanning transmission electron microscopy (STEM), where a converged electron probe is scanned over a sample's surface and an imaging, diffraction, or spectroscopic signal is measured as a function of probe position, is an extremely powerful tool for materials characterization. The widespread adoption of hardware aberration correction, direct electron detectors, and computational imaging methods have made STEM one of the most important tools for atomic-resolution materials science. Many of these imaging methods rely on accurate imaging and diffraction simulations in order to interpret experimental results. However, STEM simulations have traditionally required large calculation times, as modeling the electron scattering requires a separate simulation for each of the typically millions of probe positions. We have created the Prismatic simulation code for fast simulation of STEM experiments with support for multi-CPU and multi-GPU (graphics processing unit) systems, using both the conventional multislice and our recently-introduced PRISM method. In this paper, we introduce Prismatic version 2.0, which adds many new algorithmic improvements, an updated graphical user interface (GUI), post-processing of simulation data, and additional operating modes such as plane-wave TEM. We review various aspects of the simulation methods and codes in detail and provide various simulation examples. Prismatic 2.0 is freely available both as an open-source package that can be run using a C++ or Python command line interface, or GUI, as well within a Docker container environment
Recommended from our members
py4DSTEM: a software package for multimodal analysis of four-dimensional scanning transmission electron microscopy datasets
Scanning transmission electron microscopy (STEM) allows for imaging,
diffraction, and spectroscopy of materials on length scales ranging from
microns to atoms. By using a high-speed, direct electron detector, it is now
possible to record a full 2D image of the diffracted electron beam at each
probe position, typically a 2D grid of probe positions. These 4D-STEM datasets
are rich in information, including signatures of the local structure,
orientation, deformation, electromagnetic fields and other sample-dependent
properties. However, extracting this information requires complex analysis
pipelines, from data wrangling to calibration to analysis to visualization, all
while maintaining robustness against imaging distortions and artifacts. In this
paper, we present py4DSTEM, an analysis toolkit for measuring material
properties from 4D-STEM datasets, written in the Python language and released
with an open source license. We describe the algorithmic steps for dataset
calibration and various 4D-STEM property measurements in detail, and present
results from several experimental datasets. We have also implemented a simple
and universal file format appropriate for electron microscopy data in py4DSTEM,
which uses the open source HDF5 standard. We hope this tool will benefit the
research community, helps to move the developing standards for data and
computational methods in electron microscopy, and invite the community to
contribute to this ongoing, fully open-source project